iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge

TL;DR

This paper introduces an open-source framework that automatically constructs up-to-date, disease-specific semantic knowledge graphs from biomedical literature and resources, facilitating targeted research and analysis.

Contribution

The study presents a novel automated method for building disease-specific semantic graphs, integrating diverse knowledge sources into a unified, current resource.

Findings

Successfully applied to three diseases: Lung Cancer, Dementia, Duchenne Muscular Dystrophy

Enables targeted retrieval and analysis of disease-specific knowledge

Provides an open-source tool for continuous knowledge graph updates

Abstract

In biomedical research, unified access to up-to-date domain-specific knowledge is crucial, as such knowledge is continuously accumulated in scientific literature and structured resources. Identifying and extracting specific information is a challenging task and computational analysis of knowledge bases can be valuable in this direction. However, for disease-specific analyses researchers often need to compile their own datasets, integrating knowledge from different resources, or reuse existing datasets, that can be out-of-date. In this study, we propose a framework to automatically retrieve and integrate disease-specific knowledge into an up-to-date semantic graph, the iASiS Open Data Graph. This disease-specific semantic graph provides access to knowledge relevant to specific concepts and their individual aspects, in the form of concept relations and attributes. The proposed approach is implemented as an open-source framework and applied to three diseases (Lung Cancer, Dementia, and Duchenne Muscular Dystrophy). Exemplary queries are presented, investigating the potential of this automatically generated semantic graph as a basis for retrieval and analysis of disease-specific knowledge.